Angela D.R. Smith
National Science Foundation
06/15/2023 to 05/31/2028
The award is $1,368,414 over the project period.
Collaborative Research: Racial Equity: Engaging MarginalizedGroups to Improve Technological Equity
This collaborative project investigates the lack of diverse, representative datasets and insights in the development and use of technology. It explore the effects of disparities on the ability of technologists (e.g., practitioners, designers, software developers) to develop technology that addresses and mitigates systemic societal racism and historically marginalized individuals' ability to feel seen and heard in the technology with which they engage. The implications of this project are threefold: 1) it supports building relationships between technologists and technology users by understanding the values that most impact historically marginalized communities' engagement and data contributions; 2) given access to more diverse data and insights, the project provides technologists with interventions that empower them to make use of these data and insights in practice; 3) lastly, the work provides support and affirmation for the technologists who are already making these explicit considerations in their work without the adequate support. More broadly, insights from this project can be applied in practice to promote racial equity and ensure systemic racism is an explicit consideration in STEM education and workforce development by incorporating more equitable practices in technologists' workflow.
This study seeks to answer three main research questions: 1) What are the barriers to engaging and amplifying marginalized voices in technological spaces and data sets for both technologists and users? 2) How can marginalized groups be engage when designing and developing data-centric systems without sacrificing their safety, security, and trust? 3) What does it look like to provide interventions for engaging the margins to technologists without compromising the safe spaces for marginalized groups? Using a multi-modal approach, the project will examine how researchers and technologists can best learn to engage in data-centric research with marginalized communities in an ethically and socially responsible manner that centers the rights and values of the communities of interest. Culturally relevant approaches and grounding philosophies will drive the research methods and analyses. Through surveys, semi-structured interviews, design workshops utilizing a combination of participatory design and community-based approaches, as well as case study analysis to collect qualitative and quantitative data, the research team will develop an intervention that supports technologists in responsible engagement. Aside from real-world implementation, this project will share its findings through academic and community-facing venues, such as journal publications, conference presentations, op-eds, blogs, workshops, and social media.
This collaborative project is funded through the Racial Equity in STEM Education program (EDU Racial Equity). The program supports research and practice projects that investigate how considerations of racial equity factor into the improvement of science, technology, engineering, and mathematics (STEM) education and workforce. Awarded projects seek to center the voices, knowledge, and experiences of the individuals, communities, and institutions most impacted by systemic inequities within the STEM enterprise. This program aligns with NSF's core value of supporting outstanding researchers and innovative thinkers from across the Nation's diversity of demographic groups, regions, and types of organizations. Programs across EDU contribute funds to the Racial Equity program in recognition of the alignment of its projects with the collective research and development thrusts of the four divisions of the directorate.
Ying Ding
Yan Leng, and Samuel Craig Watkins, University of Texas at Austin;
Yifan Peng Weill Cornell Medicine
AIM-AHEAD and National Institutes of Health (NIH)
09/17/2023 to 09/16/2025
The collaborative award is $998,739 over the project period. The School of Information portion of the award is $698,739.
Closing the loop with an automatic referral population and summarization system
Suicide is a public health concern and is ranked as the second leading cause of death in 10-24 years old.1,2 In particular, the increasing rates of suicide mortality and suicidal ideations and behaviors among Black youth in the United States (US) have become a pressing concern in recent years.3 Between 2001 and 2015, Black children under 13 years old were twice as likely to die by suicide, compared to their White counterparts.4 Furthermore, suicide mortality rates among Black youth have risen more rapidly than in any other racial or ethnic group.2,5 However, there remains a significant knowledge gap in understanding culturally tailored suicide prevention strategies for this population, particularly regarding unique social risk factors specific to Black youth. Specifically, a detailed understanding of social risk factors unique to Black youth and their differentiation from risk factors for other racial and ethnic groups is limited.2 This knowledge gap is critical, as research has indicated that Black youth face greater exposure to adverse childhood experiences (ACEs), which are linked to higher risks of suicidal ideation and attempts.
The National Violent Death Reporting System (NVDRS) is a state-based violent death reporting system in the U.S. that helps provide information and context on when, where, and how violent deaths occur and who is affected.11 However, much of the information in NVDRS is unstructured, limiting its use in examining a complete picture of the social risk factors contributing to Black youth suicide. Therefore, it is imperative to develop machine learning (e.g., natural language processing [NLP]) algorithms to automatically extract social risk factors from free text to help analyze Black youth suicide. Our long-term goal is to reduce the suicide rate by developing novel interventions targeting risk and protective factors among Black youth. The overall objective of this application is to develop and validate new AI approaches to identify individual-level social risks of Black youth suicide and enhance trust within the underserved communities regarding the approaches of AI/ML.
Ying Ding
led by Yifan Peng Weill Cornell Medicine
National Institutes of Health (NIH)
08/01/2023 to 04/30/2028
The collaborative award is $712,024 over the project period. The School of Information portion of the award is $333,944.
Closing the loop with an automatic referral population and summarization system
In the United States, more than a third of patients are referred to a specialist each year, and specialist visits constitute more than half of outpatient visits. Even though all physicians highly value communication between primary care providers (PCPs) and specialists, both PCPs and specialists cite the lack of effective information transfer as one of the most significant problems in the referral process. Therefore, it is critical to investigate a new method to improve communication during care transitions. With their ubiquitous use, it is recognized that electronic health records (EHRs) should ensure a seamless flow of information across healthcare systems to improve the referral process. But, a lack of accessible and relevant information in the referral process remains a pressing problem. Recently, emerging deep learning (DL) and natural language processing (NLP) methods have been successfully applied in extracting pertinent information from EHRs and generating text summarization to improve care quality and patient outcomes. However, existing technologies cannot be applied to process heterogeneous data from EHRs and create high-quality clinical summaries for communicating a reason for referral. Responding to PA-20-185, this project will develop and validate a novel informatics framework to collect and synthesize longitudinal, multimodal EHR data for automatic referral form generation and summarization. While the referring provider and specialist can be any type of provider for any condition, the focus in this application has been on headache for primary care, because it is an extremely common symptom and affects people of all ages, races, and socioeconomic statuses. More importantly, relevant information needed for headache referrals has been defined in local and national evidence-based practice guidelines. Therefore, a health information technology solution to make these data accessible will empower communication between PCPs and specialists, which can improve the care of millions of patients suffering from disabling headache disorders. Based on our preliminary data and our experience with an interdisciplinary team of data scientists and physicians, we plan to execute specific aims: 1) Convert text-based guidelines into a standards-based algorithm for electronic implementation; 2) develop models to automatically populate data from EHR and clinical notes to fill the referral form; 3) create a framework to summarize the longitudinal clinical notes to fill out the referral form; and 4) develop and validate the headache referral system with a user-centered design approach. The research proposed in this project is novel and innovative because it will produce and rigorously test new solutions to improve the communication between health professionals to ensure that safe, high-quality care is provided and care continuity is maintained. The success of this project will (1) fill important gaps in our knowledge of understanding the types of information exchange that will optimize patient care during transitions and (2) provide evidence-based solutions to enable the exchange.
James Howison
Jennifer Schopf, Angela Newell, and Michael Shensky
Alfred P. Sloan Foundation
08/01/2023 to 07/31/2025
The award is $650,000 over the project period.
University of Texas Open Source Program Office
The University of Texas Open Source Program Office (UT-OSPO) is the center for open source activity, connection, training, and support to enable open source practices as a key part of the university mission. With financial support from the Alfred P. Sloan Foundation, this project is led by personnel from UT Austin’s central IT services, Libraries, iSchool, and TACC in order to form an umbrella organization that is more than the sum of its pieces.
The UT-OSPO coordinates a shared open infrastructure for software development, establishing a central hub for open source support that enables the university to leverage and formalize the pre-existing infrastructure on campus, unify and expand the work already being done in this space, create additional opportunities for engagement among faculty and students, and foster interdisciplinary connections across departments and units.
This infrastructure promotes more reproducible and open research through the development of an ecosystem of researchers engaging and growing open source skills and practice through a pathway of participation. We provide support through:
- joint training
- personalized consultations
- lecture series
- a help desk network
- publishing of best practices, and
- events that help students, faculty, and staff engage with open source software.
Min Kyung Lee
Carin Håkansta (Karolinska Institutet)
Karolinska Institutet
07/01/2023 to 12/31/2025
The School of Information allocation from this collaborative award is $28,381.
ALGOSH: Algorithmic management at work - challenges, opportunities, and strategies for occupational safety and health and wellbeing
Algorithms are at the forefront of a transformative shift in the World of Work, profoundly influencing work dynamics, organizational structures, and the work environment. Despite their profound impact, a substantial knowledge gap exists concerning algorithmic management (AM) and its repercussions on occupational safety, health, and wellbeing. This gap is particularly pronounced in non-platform work settings, where AM's prevalence is growing.
As the use of AM continues to expand across various economic sectors, it is imperative to investigate its effects on the wellbeing of workers. The overarching objective of the ALGOSH research program is to enhance our understanding of AM in non-platform sectors and its impact on the health, safety, and wellbeing of workers. Moreover, it aims to develop tools and strategies to mitigate associated risks. The three research aims of ALGOSH are:
- Facilitating the development of a standard for measurement of algorithmic management at work and related risks for health, safety and well-being.
- Increasing knowledge about the effects of algorithmic management on workers’ health, safety, and well-being.
- Investigating the balance of interests related to the control of algorithms in different legal contexts regarding occupational health and safety (OSH).
To accomplish this mission, an international and interdisciplinary consortium of researchers has been assembled. For our research to have maximum societal impact, the program also has a strong stakeholder involvement and support from trade unions, business organizations, international bodies, and government agencies. Their collective efforts will examine, discuss, and assess the opportunities and challenges posed by algorithmic management, fostering a safer and healthier work environment for all. The program applies multiple methods including quantitative, qualitative, literature reviews and participatory research.